Chatbots that actually close.
Not the "how can I help you today?" toy. Real GPT-4 assistants trained on your product, policies and price list — qualifying leads at 2am and cutting support tickets in half.
Where a chatbot pays for itself.
Customer support
Deflect 40–70% of tickets by answering documented questions instantly.
Lead qualification
Ask the right 3–5 questions, score intent, book a call automatically.
Product finder
Guide shoppers to the right SKU via conversation, not filters.
Internal helpdesk
HR policies, IT tickets, expense rules — answered from your docs.
How we build it.
Discover
We interview your team, collect docs, and map the conversations that repeat.
Build knowledge base
Chunk, embed, and index your content in a vector store with metadata filters.
Design flows
Draft prompts, guardrails, and handoff logic. Test against 100+ real questions.
Ship & measure
Deploy to your site or WhatsApp with analytics, feedback capture and retraining.
Safe by default.
Every chatbot we ship has guardrails baked in — because "the AI said something weird" is not a bug you want in production.
- Trained only on your data
- Human-in-the-loop escalation
- GDPR-aware logging
- PII redaction by default
- Rate-limited to control cost
- Weekly retraining reports
Chatbot FAQs.
We default to OpenAI GPT-4.1 or Claude Sonnet for reasoning, with Gemini as a cost-optimized fallback. We pick per use case.
Intent classification vs LLM routing
The old rule was "NLU for intents, LLM for chit-chat". The new rule depends on volume, brand risk, and how bounded your knowledge is.
| Approach | Best for | Cost per convo | Weakness |
|---|---|---|---|
| Rule tree (Dialogflow-style) | <20 intents, high volume | ₹0.01 | Brittle to phrasing; edit hell |
| Small classifier + templated reply | Structured support flows | ₹0.05 | Needs labelled data |
| RAG over knowledge base | Docs-heavy, low-risk | ₹0.80-2 | Hallucinates outside corpus |
| LLM + tool-use | Transactional (book, refund) | ₹2-6 | Requires strict function schemas |
| Agentic multi-step | Complex diagnosis | ₹4-15 | Slower; needs human fallback |
When the bot should hand off
The single biggest driver of chatbot NPS is how gracefully it fails. These are our default handoff triggers.
- Two consecutive low-confidence turns
Retrieval score below threshold or classifier confidence <0.6 → offer human immediately, don't force a third attempt.
- Explicit escalation intent
"Agent", "human", "speak to someone", frustration signals (caps, profanity) — instant handoff, no gate.
- Regulated topics
Medical, legal, financial advice, account changes — routed to a human even if the bot could answer.
- Sentiment drop mid-conversation
Track sentiment delta per turn; a sharp negative slope triggers escalation with full transcript.
- Beyond scope confidence
Router detects out-of-domain query; bot says so honestly and offers ticket creation instead of guessing.
- Post-purchase disputes
Any refund/chargeback/complaint routed to human queue with tags and priority preset.
Let's build your chatbot.
30-minute call. We'll sketch the flows and give you an honest estimate.
Book discovery callServing businesses across Rayalaseema
Founder-led website design, development and automation — for businesses in Andhra Pradesh and beyond.
Automate what's costing your team hours
Describe the workflow — we'll reply with what's possible, what it costs, and where to start.
- Personal reply from Siddiq within 24 hours
- Honest advice — no pushy sales, no fake urgency
- Clear next steps, timeline, and estimate